УДОСКОНАЛЕННЯ МЕТОДУ ВИЯВЛЕННЯ РАДІОСИГНАЛІВ ЗА ДОПОМОГОЮ ТОПОЛОГІЧНОЇ ІДЕНТИФІКАЦІЇ ЗАГРОЗ

Recently, the use of unmanned aerial vehicles for both reconnaissance and fire strikes has been increasing. The control of such vehicles and the transmission of telemetry information are carried out using a combined command and telemetry radio link and frequency hopping spread spectrum technology. In such conditions, the task of developing automatic algorithms for detecting and separating radio links for portable radio monitoring devices becomes especially relevant. The aim of this work is to extend the capabilities of portable radio monitoring devices to detect unmanned aerial vehicles’ radio signals in sophisticated electronic environment by automating signal processing. Formally, the research task is to detect and separate several frequency hopping spread spectrum signals in same frequency band. To solve this task, it is first necessary to detect the frequency channels and estimate frequency and, if necessary, temporal and modulation parameters of the signals in these channels. Estimates of signal parameters values are recorded in associative arrays. The paper proposes a scheme of the algorithm for automatic detection and separation of signals with frequency hopping spread spectrum, as well as the structure of the array with the results of frequency band analysis. In the study of developed algorithm, we considered the practically important option of separating radio links with frequency hopping spread spectrum by the spectral width of the frequency element and the value of the signal-to-noise ratio in the frequency channel, since these features can be quite easily distinguished in the frequency domain. The radio links separation was performed by cluster analysis of scatter plots of parameter pairs estimates using a Gaussian mixture model. The developed algorithm can be implemented in existing and prospective portable radio monitoring devices to automatically detect and determine the number of unmanned aerial vehicles by their radio links signals, in particular, under conditions of a priori uncertainty about the values of signal parameters.

The purpose of the research presented in the article is to improve the efficiency of conducting special studies of the DisplayPort interface based on the study of characteristics for the development of test programs. To do this, the possibility of intercepting the side electromagnetic radiation of this interface is investigated, the search for informative signals from the interface is carried out, and their attenuation at various distances is investigated. The parameters of the informative signal from the DisplayPort interface are analyzed on the oscilloscope in automatic mode. The relevance of the development of special programs is justified. To confirm the relevance of this study, the measurement of electromagnetic fields and the search for informative signals were carried out by various special technical means in the frequency range from 0.009 to 12000 MHz. Informative signals were detected at 10 frequencies from the investigated technical means, which indicates the existing technical channel of information leakage. To achieve the goal of the study, the amplitude and time parameters of the informative signal were analyzed using a digital oscilloscope. The result of the pulse duration and period is obtained, which is necessary for better and more efficient performance of special studies on the mode of information output to the monitor screen via the DisplayPort interface.

Femtosecond laser-based generation of radio frequency signals has produced astonishing improvements in achievable spectral purity, one of the basic features characterizing the performance of an radio frequency oscillator. Kerr frequency combs hold promise for transforming these lab-scale oscillators to chip-scale level. In this work we demonstrate a miniature 10 GHz radio frequency photonic oscillator characterized with phase noise better than −60 dBc Hz−1 at 10 Hz, −90 dBc Hz−1 at 100 Hz and −170 dBc Hz−1 at 10 MHz. The frequency stability of this device, as represented by Allan deviation measurements, is at the level of 10−10 at 1–100 s integration time—orders of magnitude better than existing radio frequency photonic devices of similar size, weight and power consumption.

In this paper, we present and evaluate a low-cost platform, able to emulate multiple primary and secondary users with a single Software Defined Radio (SDR) device. By effectively exploiting techniques that are applicable to OFDM, we manage to transmit simultaneously multiple DVB-T channels, along with IEEE 802.11a/g and wireless microphone signals. Utilizing a single PC with open-source software and a SDR device we are able to substitute multiple appliances, such as expensive and specialized laboratory equipment. The proposed platform can also serve as an efficient real-time multiple users testbed for complex cognitive radio (CR) scenarios and techniques validation, 5G modulations testing-prototyping, and heterogeneous wireless networking exploration.

With the development of cognitive radio, more and more radio frequency devices can dynamically switch radio frequency parameters. The reliability of the traditional method of marking the radio signal source in the communication band is greatly reduced. This paper proposes a radio frequency fingerprint feature extraction method based on the accumulated distance of I/Q data components. The RF raw I/Q sample data is collected through the distributed electromagnetic spectrum detection network. The cumulative I/Q distance is extracted from the I/Q sample data collected by multiple detection nodes. The weight distribution of the I/Q components is calculated. Based on I/Q sample data estimation cumulative I/Q distance and reception signal strength parameters, the received signal strength (RSS)-I/Q distance characteristic curve and the corresponding dataset are constructed. The radio frequency fingerprint characteristic curve of the target radio device is established. The optimized Sigmod mathematical model is used as a target function. The RSS-I/Q distance data is set as a sample. The normalized dataset is fitted according to the target function. The parameter values of the optimized sigmod mathematical model are obtained by estimating the fitted curve. The estimated value is taken as the eigenvalue matrix, which is the radio frequency fingerprint characteristic parameter of the target radio equipment. The RF fingerprint feature extraction method proposed in this paper can comprehensively analyze the subtle features of radio equipment from the perspective of signal source. The experiments show that the RF fingerprint feature parameters extracted from the same signal source in different frequency bands, different environments (cement ground, asphalt road, grass, riverside, indoor, etc.), and different temperature and humidity parameters are invariant within a certain error range. The radio frequency fingerprint feature parameters extracted in different signal sources have good distinction in the same frequency band, the same environment, and the same temperature and humidity.

Consumer-Off-The-Shelf (COTS) radio devices have shown increasing use in atypical applications. On the modern battlefield, many transmission devices utilize unencrypted, identifiable radio signals vulnerable to exploitation and disruption. This paper designs, simulates, and tests a control device capable of receiving signals from a radio beacon, identifying if the transmissions are friend or foe, and make a determination to allow or deny communication from the device. Digital beamforming with an antenna array is used to optimize reception to and from the mobile beacon. Upon identification of a friendly transmission, the device performs a handshake and allows enhanced directional communication with the target. Upon identification of an undesired communication, the device transmits interference-based jamming to efficiently and quickly disrupt possible communication signals from the uninvited source. The device performs well in medium-range scenarios and offers a unique dual-purpose for interfacing with COTS telemetry and ISM band equipment.

There is a need to fulfill the growing capacity requirements of power line communication (PLC). One way to achieve higher data rates in PLC networks is to operate at higher frequencies and increase the used bandwidth. In the future, the increased PLC frequency range can overlap with several wireless communications systems resulting in interference for these transmissions and vice versa. Commonly used static notching results in inefficient spectrum utilization as in a given place and at a given time only a part of the frequency range may be occupied by other wireless systems. Some wireless signals like FM signals are very closely spaced. In practice it is impossible to utilize these narrowband unoccupied frequency bands. In this case we can cluster closely spaced signals. Guard bands around detected wireless signals must also be taken into account before defining usable frequency bands for PLC. In this paper, we use the 2-D LAD ACC algorithm to detect and cluster interfering signals. We also further develop the algorithm so that after clustering guard bands are added and reclustering, if necessary, is executed. The performance of this modified 2-D LAD ACC algorithm is analyzed using calculations and spectrum analyzer measurement results obtained from in-office PLC network in the frequency range 80–200 MHz. The results verify that the proposed method performs well by detecting and clustering continuous and non-continuous wireless signals and providing means for clever spectrum utilization in PLC-network.

Automatic Classification of Wireless Signals (ACWS), which is an intermediate step between signal detection and demodulation, is investigated in this paper. ACWS plays a crucial role in several military and non-military applications, by identifying interference sources and adversary attacks, to achieve efficient radio spectrum management. The performance of traditional feature-based (FB) classification approaches is limited due to their specific input feature set, which in turn results in poor generalization under unknown conditions. Therefore, in this paper, a novel feature-based classifier Neural-Induced Support Vector Machine (NSVM) is proposed, in which the features are learned automatically from raw input signals using Convolutional Neural Networks (CNN). The output of NSVM is given by a Gaussian Support Vector Machine (SVM), which takes the features learned by CNN as its input. The proposed scheme NSVM is trained as a single architecture, and in this way, it learns to minimize a margin-based loss instead of cross-entropy loss. The proposed scheme NSVM outperforms the traditional softmax-based CNN modulation classifier by managing faster convergence of accuracy and loss curves during training. Furthermore, the robustness of the NSVM classifier is verified by extensive simulation experiments under the presence of several non-ideal real-world channel impairments over a range of signal-to-noise ratio (SNR) values. The performance of NSVM is remarkable in classifying wireless signals, such as at low signal-to-noise ratio (SNR), the overall averaged classification accuracy is > 97% at SNR = −2 dB and at higher SNR it achieves overall classification accuracy at > 99%, when SNR = 10 dB. In addition to that, the analytical comparison with other studies shows the performance of NSVM is superior over a range of settings.

In this paper, we review a variety of deep learning algorithms and models for modulation recognition and classification of wireless communication signals. Specifically, deep learning (DL) has shown overwhelming advantages in computer vision, robotics, and voice recognition. Recently, DL has been proposed to apply to wireless communications for signal detection and classification in order to better learn the active users for electromagnetic spectrum sharing purposes. Therefore, we aim to provide a survey on the most recent techniques which use DL for recognizing and classifying a wireless signal. We focus on the most widely used DL models, emphasize the advantages and limitations, and discuss the challenges as well as future directions. In addition, we also apply a DL algorithm, convolutional neural network (CNN), to demonstrate the feasibility of using CNN to recognize and classify the over-the-air wireless signals using Mathworks DL toolbox with PlutoSDR and Universal Software Radio Peripheral (USRP), respectively.

The article presents practical implementation of energy detection method based upon non-parametric statistics computed using periodic spectrum samples provided by measuring equipment. The method enables efficient detection and monitoring for signals with low or negative signal-to-noise ratio. The method's sensitivity is limited by measuring equipment inherent noise fluctuations and can be a priory experimentally established for certain experimental hardware settings and desirable spectrum samples lengths. Sensitivity thresholds (in terms of signal-to-noise ratio) for reliable (with probability > 0,98) signal detection for a typical spectrum analyzer used in experiment varied from –11 dB to + 0,6 dB for spectrum samples lengths ranged between 30 000 and 470 spectrums respectively. The suggested energy detection method can be used for unstable and intermittent signals detection, which are active (or above sensitivity threshold) only for a fraction of spectrum sample recording time. The method is independent of signal's modulation (if any is used), amplitude variability profile and signal's probability distribution features. Experimentally determined sensitivity threshold levels for real radio frequency signals coincided within 1,9 dB tolerances with corresponding levels estimated from spectrum analyzer inherent noise fluctuations for all implemented spectrum samples lengths. The data recording time for abovementioned spectrum samples lengths ranged between 207 and 3,2 seconds respectively and was entirely hardware-dependent parameter. Experiment proved equal efficiency and reliability of the suggested method for reliable detection for both white noise signal (generated by analog generator) and broadcasted LTE signal (generated by cellular base stations), which were affected by multi-path propagation effects and average signal level instability due to subscribers time-varying activity. The experiment showed the proposed energy detection method besides detection of low-level radio frequency signals (down to –11 dB SNR) provides highly reliable assessment of the detected signal's signal-to-noise ratio with 0,6 dB tolerance and 0,95 probability. The energy detection method demonstrated zero level of false detections when there was no signal at the spectrum analyzer input (the input port of the instrument was terminated by a matched load), which is essential for method applicability in tasks of highly reliable detection of low-level signals from various types of sources. Taking into account specifications of available hardware, required sensitivity level and limits for data recording time it is possible to choose optimal length of spectrum sample for the energy detection method, which would be the most reasonable for any task in question. The energy detection method based upon non-parametric statistics computed using periodic spectrum samples can be effectively used in detection and radiomonitoring of low-level signals, in radio frequency electromagnetic compatibility research tasks and radio propagation path properties analysis in high loss environment.

On 24 May 2023 a radio signal containing a simulated extraterrestrial message was transmitted towards Earth by the Trace Gas Orbiter, a Mars orbiting spacecraft of the European Space Agency. The signal was received by the Green Bank Telescope and the Allen Telescope Array in the United States and by the Medicina Radio Antenna in Italy. The event was part of the interdisciplinary project “A Sign in Space” and was streamed live by the SETI Institute, with thousands of people watching in real time. The project has been conceived and directed by media artist Daniela de Paulis, currently artist in residence at the Green Bank Telescope and at the SETI Institute. After the live event, the raw data of the signal were published in the public domain. Shortly after, an international group of citizen scientists gathered on the online platform Discord to work on the decoding process, and in just one week, they managed to extract the message from the raw data of the signal. This was an extraordinary achievement by the citizen scientist community who accomplished this feat by collaborating with each other. The data of the message extracted from the radio signal were visualised by the Discord community as an image composed by a black background and white dots. This image, named “Star Map” has been the object of hundreds of interpretations by many people who are trying to further decode the data and give them scientific and cultural meanings. The message however remains undecoded and the decoding process is ongoing. "A Sign in Space" has been reaching the international public. The project, designed as a work of cosmic theatre, has created an imaginary event to be experienced subjectively and collectively by people from various cultures and geographical locations. In "A Sign in Space", people active in the decoding process are members of a micro-society, engaged in the process of meaning making. The decoding process has been fostering a collaborative approach, over a competitive one. “A Sign in Space” stages a concrete "first-contact scenario", simulating the societal, scientific and cultural impact of the first detection of a radio signal from an extraterrestrial civilisation. “A Sign in Space” presents a socially innovative event, in which the general public plays a crucial role in decoding a potential extraterrestrial message, employing collective intelligence and skills. In the project, citizen scientists venture into a journey of scientific, cultural, artistic exploration, working independently and in groups. "A Sign in Space" asks how humans, as a multitude of societies with very diverse cultural backgrounds, give meaning to concepts, events, phenomena, using one of the possibly most radical scenarios that humankind might encounter: attempting to assign meaning to a message from an extraterrestrial civilisation. How can we possibly give meaning to something so detached from our terrestrial experience? 

The high number density of radio antennas at the LOFAR core in Northern Netherlands allows to detect radio signals emitted by cosmic ray induced air showers, and to characterize the geometry of the observed cascade in a detailed way. We present here a study of the radio frequency spectrum in the 30 - 80 MHz regime, and its correlation with some geometrical parameters of the extensive air shower. An important goal of this study is to find a correlation between the frequency spectrum and the primary particle type. Preliminary results on how the frequency spectrum changes as function of distance to the shower axis, and as function of primary particles mass composition are shown. The final aim of this study is to find a method to infer information of primary cosmic rays in an independent way from the well-established fluorescence and surface detector techniques, in view of affirming the radio detection technique as reliable method for the study of high energy cosmic rays.

Ambient backscatter communications, a promising technique to realize massive machine type communication (mMTC), has recently attracted great attentions, due to its spectrum-and-energy-efficient characteristics. In ambient backscatter communications, multiple tags will transmit signals asynchronously to the target reader, which however imposes huge challenges to the radio access and signal detection at the reader. To tackle these problems, we propose an independent component analysis (ICA) based blind signal separation, identification and detection scheme. Specifically, each of tag signals is randomly and independently encoded to reduce the collision of the tags. Then a novel ICA algorithm is applied at the reader to separate, identify and detect the signals. The results show that the proposed scheme, compared with existing schemes, provides higher detection accuracy with lower cost even under a large number of tags.

The option of creating an information leakage channel using high-frequency radiation (for example, auxiliary technical means or semi-active stowage devices installed indoors) is one of the active methods for obtaining acoustic speech information. This paper provides a rationale for the requirements for technical characteristics and the scheme for constructing a device for detecting high-frequency radiation signals. It serves to develop a software and hardware complex for the study of technical channels of information leakage, based on the method of high-frequency irradiation. The ultimate goal is to create a prototype of a means for detecting and suppressing receiving devices of high-frequency irradiation equipment based on software-defined devices. The paper describes an approach to the creation of the hardware and software parts of the complex, presents the results of studies on the detection of signals in a wide frequency range.

A robot is an automatically guided machine which is able to do tasks on its own, almost always due to electronically ogrammed instructions Radio control (often abbreviated to R/C or simply RC) is the use of radio signals to remotely control a device. This is often a radio control device, cable between control and vehicle, or an infrared controller. Our paper focus on operating robots by cell phone in order to simplify the contro of robots. Methodology used is DTMF (Dual Tone Multi-Frequency). All over it is used frequently to refer to the control of model vehicles from a hand-held radio transmitter. Industrial, military, and scientific research organizations make [traffic] use of radio-controlled vehicles as well.